---
title: "The squirrel problem"
author: "Javier Saez Gallego"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(leaflet)
library(raster)
library(plotly)
library(readxl)
library(dplyr)

# Load path and map
path_points = read.csv("Files/path_coordinates_solution.csv")
spain_img = raster("Files/GlobCover_Spain.tif")

# transform raster values to be plotted nicely
# map labels to roughness values
globCover = c(11,14,20,30,40,50,60,70,90,100,
              110,120,130,140,150,160,170,180,190,200,210,220,230)
roughness = c(0.10,  0.10,  0.30,  0.30,  1.50,  1.50,  1.50,  1.50,  1.50,  1.50,
              1.50,  0.50,  0.10,  0.03,  0.05,  0.50,  0.60,  0.20,  1.00,  NA,  NA,  NA,  NA)
small_img_plot = reclassify(spain_img,cbind(globCover,roughness))

```

Map
=======================================================================


```{r}
# The raster is too big to be displayed so let's shrink it
agg_small_img_plot = raster::aggregate(small_img_plot,fact=3)

# Make a leaflet visualization
pal2 = colorNumeric("RdYlGn", domain = NULL)

ll_map =  leaflet() %>%
  # Base maps
  addProviderTiles('Esri.WorldGrayCanvas',group="Esri Grey") %>%
  addProviderTiles('Esri.WorldTopoMap',group="Esri Topo") %>%
  addProviderTiles("Esri.WorldImagery", group = "Esri Image") %>%
  # # Roughness info
  addRasterImage(agg_small_img_plot, opacity = 0.5,colors=pal2, group="Roughness map") %>%
  addLegend(position="bottomleft", pal = pal2, values = values(agg_small_img_plot),
  title = "Roughness",
  opacity = 1) %>%
  # path info
  addPolylines(lng=path_points$lon,lat=path_points$lat,group="Path") %>% 
  # Control groups
  addLayersControl(
    baseGroups = c("Esri Grey","Esri Image","Esri Topo"),
    overlayGroups = c("Roughness map", "Path"),
    options = layersControlOptions(collapsed = F)
  )
ll_map
```



Path statistics
=======================================================================


Row
-----------------------------------------------------------------------

### 

```{r, echo=FALSE, message=FALSE, warning=FALSE}

# Get values of the pixels where the squirrel steps
values_glob = values(spain_img)[path_points$n]
tab_summary = sort(table(values_glob))
tab_labels = as.numeric(names(tab_summary))

# Create some nice labels for each type of land
legend = read_excel("Files/Globcover2009_Legend.xls")
idx = mapply(function(x) which(x == legend$Value), tab_labels)

# Prepare colors
pal2 = colorNumeric("RdYlGn", domain = c(0,1.5))
idx2 = mapply(function(x) which(x == globCover), tab_labels)
col_roughness = pal2(roughness[idx2])

# Make the plot
plot_ly( y = tab_summary, x = names(tab_summary),
        text = legend$Label[idx],
        type = 'bar', hoverinfo = 'text',
        marker = list(color = col_roughness)) %>%
  layout(title = "Land cover along the shortest path",
         xaxis = list(title = "Land cover type"),
         yaxis = list(title = "count"))

```


Row
-----------------------------------------------------------------------

### 


```{r, echo=FALSE, message=FALSE, warning=FALSE}
# Get the colors
values_glob_plot = values(small_img_plot)[path_points$n]
col_roughness_all = pal2(values_glob_plot)

# Get the hover text
idx = mapply(function(x) which(x == legend$Value), values_glob)
text_hover = legend$Label[idx]

# Make the plot
plot_ly( x=1:length(values_glob_plot),y = values_glob,
         marker=list(color =col_roughness_all ),
         text = text_hover) %>%
  layout(title = "Land cover along the shortest path",
         xaxis = list(title = "Step number"),
         yaxis = list(title = "Land cover type"))

```


Info
=======================================================================


> Can a squirrel cross from north to south spain without touching the ground?

The answer is: **obviously not**. But, another question arises, and this one is not so easy to answer:

> If a squirrel had to go from the north of Spain to the south, touching the ground as little as posisble: which way would it follow?

The answer is given here!

The code is in [this](https://github.com/jsga/GlobCover_maps_squirrel) repository. Feel free to share and comment.